{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the time series, just a simple AR(1)\n",
    "t1 = [0.1*np.random.normal()]\n",
    "for _ in range(100):\n",
    "    t1.append(0.5*t1[-1] + 0.1*np.random.normal())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the time series that is granger caused by t1\n",
    "t2 = [item + 0.1*np.random.normal() for item in t1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adjust t1 and t2\n",
    "t1 = t1[3:]\n",
    "t2 = t2[:-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16e4277dfc8>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(t1, color='b')\n",
    "plt.plot(t2, color='r')\n",
    "\n",
    "plt.legend(['t1', 't2'], fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_df = pd.DataFrame(columns=['t2', 't1'], data=zip(t2,t1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t2</th>\n",
       "      <th>t1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155416</td>\n",
       "      <td>0.098322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.022086</td>\n",
       "      <td>0.095749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.065441</td>\n",
       "      <td>-0.036119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.239062</td>\n",
       "      <td>-0.105903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.076154</td>\n",
       "      <td>-0.034917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.042752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>-0.105530</td>\n",
       "      <td>0.047093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.171263</td>\n",
       "      <td>0.030124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-0.014759</td>\n",
       "      <td>-0.093222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.078611</td>\n",
       "      <td>-0.076502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          t2        t1\n",
       "0   0.155416  0.098322\n",
       "1   0.022086  0.095749\n",
       "2  -0.065441 -0.036119\n",
       "3   0.239062 -0.105903\n",
       "4  -0.076154 -0.034917\n",
       "..       ...       ...\n",
       "93  0.130000  0.042752\n",
       "94 -0.105530  0.047093\n",
       "95  0.171263  0.030124\n",
       "96 -0.014759 -0.093222\n",
       "97  0.078611 -0.076502\n",
       "\n",
       "[98 rows x 2 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.3711  , p=0.5439  , df_denom=94, df_num=1\n",
      "ssr based chi2 test:   chi2=0.3830  , p=0.5360  , df=1\n",
      "likelihood ratio test: chi2=0.3822  , p=0.5364  , df=1\n",
      "parameter F test:         F=0.3711  , p=0.5439  , df_denom=94, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.7496  , p=0.4754  , df_denom=91, df_num=2\n",
      "ssr based chi2 test:   chi2=1.5816  , p=0.4535  , df=2\n",
      "likelihood ratio test: chi2=1.5688  , p=0.4564  , df=2\n",
      "parameter F test:         F=0.7496  , p=0.4754  , df_denom=91, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=43.0289 , p=0.0000  , df_denom=88, df_num=3\n",
      "ssr based chi2 test:   chi2=139.3549, p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=85.7812 , p=0.0000  , df=3\n",
      "parameter F test:         F=43.0289 , p=0.0000  , df_denom=88, df_num=3\n"
     ]
    }
   ],
   "source": [
    "gc_res = grangercausalitytests(ts_df, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
